Utilization of BIM platform for automatized energy

IN DEGREE PROJECT CIVIL ENGINEERING AND URBAN MANAGEMENT,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2020

Utilization of BIM platform for automatized energy certification scheme

UDAYAN DOIFODE

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT

Utilization of BIM platform for

automatized energy certification

scheme

Master thesis

Submitted by: Udayan Doifode.

Supervised by: Kjartan Gudmundsson.

Date: 18 December 2020

ii

Acknowledgements

It has been said that automation is the key to the future, but still, the construction industry is

working with conventional methods. Thus, to raise awareness, an attempt had been made by

proposing automation through the most trending topic of the construction industry known as

BIM. For achieving this goal, proper guidance was needed; therefore, I want to thank

Assoc. Prof. Kjartan Gudmundsson and Prof. Ivo Martinac for giving me enormous support in

this journey. Special thanks to Andrei Lițiu (PhD Candidate, KTH) for providing valuable help

from time to time.

And finally, I would thank KTH for providing all required resources throughout this project.

iii

Abstract Building information modelling (BIM) has been discussed as the most prominent tool for

bringing advancement in the construction industry. BIM provides a platform to integrate

multiple disciplinary such as structural design, architectural design, MEP details and many

more into one model, known as the BIM model. During the course of this work it was noticed

that there is a spike in developing new methodologies for efficient energy calculation of

buildings, but these methodologies are still based on the manual procedure. Example of such

methodology is the upcoming EU parameter known as Smart readiness indicator (SRI).

Therefore, an aim has been made by the author to propose automation of such energy

calculation methodology aided by BIM technology.

For this thesis work, a pre-existing Revit based BIM model was selected and structurally

modified as per the requirements. Then, the smart services from the SRI calculation method

were assigned to the Revit model. Later with the help of Dynamo (i.e. Revit plugin), a script

was created to extract smart services information from the Revit model and forward it into MS-

Excel file to obtain final SRI score. It was found that results obtain through manual calculation

and method proposed by the author were the same. Hence, it is concluded that the automation

of new energy certification scheme SRI is possible.

Keywords: Building information modeling (BIM), Smart readiness indicator (SRI), Revit,

Dynamo, Automation.

iv

Table of Contents 1. Introduction................................................................................................................. 1

1.1 BIM evolution ...................................................................................................... 2

1.2 SRI....................................................................................................................... 3

1.3 Aim & Objectives ................................................................................................. 4

1.4 Limitation............................................................................................................. 4

2. Theory ........................................................................................................................ 5

2.1 BIM tools and it's market ...................................................................................... 5

2.2 LOD in BIM ......................................................................................................... 6

2.3 Revit background.................................................................................................. 6

2.3.1 Revit Families ............................................................................................... 6

2.3.2 Revit Hierarchy ............................................................................................. 7

2.3.3 Revit Parameters............................................................................................ 7

2.4 Dynamo background ............................................................................................. 8

2.4.1 Dynamo workflow ......................................................................................... 8

2.5 EPBD calculation process of SRI assessment ......................................................... 9

2.5.1 Use of MS-Excel for user interface ............................................................... 12

3. Methodology ............................................................................................................. 13

3.1 Selection of the Revit model ............................................................................... 13

3.2 Initial data for SRI excel file ............................................................................... 14

3.3 Allocation of Simplified method A services into the Revit model ......................... 15

3.4 Dynamo script for Revit to SRI excel sheet.......................................................... 16

4. Results & Discussion ................................................................................................. 18

4.1 Validating the obtained result .............................................................................. 18

4.2 BIM interoperability ........................................................................................... 19

4.3 Effect of LOD..................................................................................................... 19

5. Conclusion ................................................................................................................ 20

6. Future scope .............................................................................................................. 21

References ....................................................................................................................... 22

Annex - I .......................................................................................................................... 24

Appendix - A.................................................................................................................... 29

Appendix - B.................................................................................................................... 30

Appendix - C.................................................................................................................... 32

v

List of figures Figure 1: Evolution of methodologies in AEC industries ...................................................... 2

Figure 2: Key areas focus under SRI (Verbeke, Aerts, et al. 2020)........................................ 4

Figure 3: Available BIM tools in market.............................................................................. 5

Figure 4: Revit Hierarchy ................................................................................................... 7

Figure 5: Dynamo workflow ............................................................................................... 8

Figure 6: SRI structure (Verbeke , Ma , et al. 2017) ............................................................. 9

Figure 7: Original Revit model.......................................................................................... 13

Figure 8: Actual Revit model used in this project ............................................................... 14

Figure 9: Example of Family Type node............................................................................ 16

Figure 10: Example of Code Block used for retrieving heating related service functionality

level ................................................................................................................................. 16

Figure 11: Example of GetParameterValueByName node .................................................. 17

Figure 12: Complete Dynamo script .................................................................................. 17

Figure 13: Results obtain through manual process.............................................................. 18

Figure 14: Result obtain through proposed semi-automated process ................................... 18

vi

List of tables

Table 1: Classification of parameters (Molinos 2016) .......................................................... 7

vii

Acronyms

BIM - Building information modelling

SRI - Smart readiness indicator

EC - European commission

EPBD - Energy performance of buildings directive

AEC - Architecture, Engineering and Construction

CAD - Computer-aided design

LOD - Level of Development

TBS - Technical building systems

BACS - Building automation & control system

1

1. Introduction

Today Building Information Modeling or BIM presents a new set of challenges and opportunities for the AEC industry. This platform requires an approach that considers all the

integrated components of any project and not merely its connection with the construction side. The BIM industry is evolving fast, and it has always been seen that such evolution is only triggered by its customers' requirements and concerning common trends. Let us look at some of the trends that are supposedly driving these changes and the opportunity in the AEC industry:

Today, 55% of the world's population lives in urban areas, a proportion that is expected to increase to 68% by 2050 (United Nations, 2018). Even the UN projected that India, China, and Nigeria would account

for 35% of the world's urban population's projected growth between 2018 and 2050. By 2050, India will add 416 million urban residents, China 255 million, and Nigeria 189 million (United Nations, 2018).

AEC projects are getting more involved with increasing sustainability requirements, and collaboration between projects are becoming more complicated. Therefore, it results in a need of generating of path for coordination and communication of design, materials, schedules, etc.

within project and compliance it across a diffused team of stakeholders. There is also an explosion of new technology, including cloud computing and mobile access. It helps to change the way we work by

giving us greater access to ever-growing amounts of data. Powerful computing capability makes analysis possible and earlier sets expectations for realistic visualizations and creates even more data to manage.

The availability of natural sources, such as energy, water, materials, and land, is decreasing. In precise, energy and water are the main components in the cost of operations for companies and municipal

bodies. So, investors, customers, and communities are interested in changing to more efficient approaches to constructing and managing their Buildings and infrastructure over the lifecycle.

The present market is intensely competitive. The fluctuating international market always dramatically affects new development and changed expectations for the bottom line. Owners and AEC service vendors need to acquire extra with much less and deliver tasks faster

and with much less environmental impact to compete and win.

At present we are witnessing global trends in the adoption of building information modeling technologies as these trends together indicate a need for some powerful platform that can serve

all purposes, and therefore BIM can be the ultimate answer. BIM is not just happening in selected countries, and it is not just about the software; what we see today is a global paradigm shift towards a different process for managing the lifecycle of all assets in the built environment. BIM adoption might vary by country, but the consistent theme is about growing

awareness, exploration use, and adoption of BIM processes globally.

Urbanization

Resources

Competitions

Complexity

Data

2

1.1 BIM evolution

Figure 1: Evolution of methodologies in AEC industries

BIM involves developing and communicating project decisions using smart 3D models, so we have transitioned from physical model making to drawing boards for 2D hand drawing and sketching, then to graphic tablets and CAD designing. Moreover, now we reached a world of

intelligent, data-rich modeling process which carries lots of potentials. Figure 1 represents this transition. BIM originated as a term to describe a technology, but the term has evolved as the industry has

begun to understand BIM's integrated nature (Latiffi, et al., 2014). As per (Gu & London, 2010), BIM is an IT empowered approach that includes applying and keeping up a fundamentally advanced representation of all building data for different stages of the project lifecycle within the form of a data storage. Later (Eastman, et al., 2011) suggested BIM as a

verb or an adjective phrase to describe tools, processes, and technologies facilitated by digital, machine-readable documentation about a building, its performance, its planning, its construction, and later its operation. In 2015, the National Building Information Model Standard defined BIM as a digital representation of a facility's physical and functional

characteristics. It is a common knowledge platform for information on a facility that forms a credible foundation for decisions across its lifecycle, described as occurring from early conception to demolition (National Institute of Building Sciences, 2019). Recently (Pakhale & Pal, 2020) defined BIM as a building of information in the 3D model and making it data-rich

with graphical and non-graphical information. Data such as dimensions, volume, geo-coordinates, asset tagging are added in the 3D model through various phases of the project life cycle, i.e., from the initial design concept until the operation and maintenance phase. In one of the BIMcert publication, BIM is defined as a method based in modern digital technology,

mainly a 3D model enriched with data that is a digital twin of a physical asset, and associated set of additional tools and processes that can, among other things, used to support sustainability trends in the construction sector (Mcauley & Behan, n.d.).

Therefore, today BIM can be described as one process that is model-centric and enabled by technology. BIM is not a purchasable or downloadable product but is a methodology supported by a technological tool kit in helping the construction industry drive clarity on the component of the changes in technology. BIM does not stop at 3D only; if time-related information is

added to this data-rich 3Dmodel, it makes a 4D BIM model. Again, if cost-related information

Physical modelling

Hand drafting

Computer-aided design (CAD)

Building Information Modelling (BIM)

3

is added to the same 4D model, the final model is termed into 5D BIM. Therefore, BIM can also be considered an advanced multi-dimensional platform.

Based on recent research, the benefits of BIM can be summarized as follows: • Provide effective collaboration between different actors and data integrity (Ellis, 2006) • Enable intelligent documentation (Popov, et al., 2006) • Provide an efficient platform for employment information requirement

• BIM models can be used for different analysis, multidisciplinary planning, and coordination (Haymaker, et al., 2005)

• As per 71-72% of BIM adopters, BIM reduces project risk and increase productivity (Bain, 2020)

As per (Fischer & Agrawal, 2020), a digital twin is a virtual representation of the physical world, which enables us to establishes a bidirectional link between physical and virtual reality. Therefore, based on BIM methodology and benefits, BIM can help the AEC industries to

generate a structural Digital twin. This digital twin can then further help to understand different buildings' scenarios and plan for future events.

1.2 SRI As per EPBD (European Commission, 2019), the current building stock within the EU is responsible for 40% of its total energy consumption and 36% of its greenhouse gas emissions.

EPBD also states that almost 75% of buildings from the following stock are energy inefficient. Therefore, a strong need to increase investment in building restoration and exploit smart and energy-efficient building technology generates. A smart building incorporates cutting-edge ICT-based solutions to maximize energy-efficient management of technical building structures

as part of their standard service. These smart capabilities will further help to construct more convenient buildings that respond to consumer and energy system requirements while growing energy use and reduce carbon impacts.

To enable the energy-efficient pattern in buildings along with different smart attributes, in 2017, EPBD proposed the development of new voluntary certification schemes known as Smart readiness indicator (SRI) (European Parliament, Council of the European Union, 2018). Later from 2018, the task for developing SRI methodology was undertaken by a consortium of VITO,

Waide Strategic Efficiency, ECOFYS, and OFFIS for the first technical study and then by VITO and Waide Strategic Efficiency Europe for the final technical study (Vito, 2017). On 22 September 2020, VITO published the final report of complete SRI methodology, which is used in this thesis work and which will also be used for future SRI assessment.

As per EPDB, the smartness of a building is defined as ‘The ability of a building or its systems to sense, interpret, communicate and actively respond in an efficient manner to changing conditions with the operation of technical building systems or the external environment

(including energy grids) and to demands from building occupants’ (Verbeke, et al., 2020). SRI thus offers information on the building's technological ability to communicate with their inhabitants and electricity grids and their operating capabilities by increasing its performance by ICT integration, which can be seen in Figure 2.

4

Figure 2: Key areas focus under SRI (Verbeke, et al., 2020)

More significant usage of smart technology can be considered to contribute to valuable, cost-effective energy conservation while enhancing indoor comfort to help the building respond to consumer needs. As smart buildings had already been recognized as the main facilitators of

potential energy networks, they also carry a top share in renewables, distributed supply, and demand-side energy flexibility. Therefore, the SRI scheme is expected to promote smart buildings' awareness – particularly

with an energy perspective. Thus, it will encourage smart building technologies and promote technological advancement in the construction sector. SRI is also expected to establish a strong bond between energy, buildings, and other divisions, especially in the ICT region. Nevertheless, it should be noted that SRI's overall theme is to only assess the available

smart services and not their actual performance.

1.3 Aim & Objectives This thesis aims to use the BIM platform for SRI assessment with the following objectives:

• To develop a semi-automatic SRI calculation process.

• To explain digital changes happening with the BIM model during its modification.

• To explain the BIM interoperability.

1.4 Limitation There are three methods suggested by the SRI committee for SRI score calculation, i.e.,

Simplified method A, Expert SRI assessment method B, and In-use smart building performance method C. However, the scope of this thesis is only limited to Simplified method A.

5

2. Theory

2.1 BIM tools and it's market

As BIM is in trend, it has attracted many software companies to develop their tools. Many of these tools are developed either as a successor of a competitor company by overcoming limitations or as a benchmark to explore BIM potential. Based on the BIM handbook by (Eastman, et al., 2011), The contractors' guide to BIM (America, A. G. C, 2006), BIM wiki

(Designing building Wiki, 2020), NBS National BIM Report 2017 (NBS, 2017) and (Omar, et al., 2014) study following classification of BIM tools has been performed:

Figure 3: Available BIM tools in the market

As per Figure 3, a large number of tools are available for Architectural, Structural, and MEP design. But in the market, the most dominant players for developing BIM tools are Autodesk Inc., Graphisoft, Nemetschek Group, and Tekla- A Trimble solution and their most popular BIM tools are Revit, ArchiCAD, Allplan, and Tekla Structures, respectively. After

understanding the functionality of many tools, Revit and Dynamo (i.e., Revit plugin) are selected as design authoring tools on this project.

6

2.2 LOD in BIM A concept that is needed to understand the BIM model in depth is LOD. LOD, i.e., Level of Development of a digital object, indicates how much detailed information it carries. As per (Reinhardt & Bedrick, 2019), LOD had been classified as LOD 100, LOD 200, LOD 300, LOD

350, and LOD 400, where a higher LOD number indicates a more detailed object. These classifications for levels of development are not only referring to the geometrical information but also the non-geometrical information.

2.3 Revit background Revit was initially founded as Charles River software company in Newton, Massachusetts in October 1997 by Leonid Raiz and Irwin Jungreis. It was built and designed specifically for architecture by architects. The company was renamed as Revit technology cooperation in

January 2000. In 2002 Autodesk purchased a computing software called Revit from Massachusetts-based Revit technologies (Conant, 2013). Today Autodesk is a significant player in The BIM market together with Nemetschek (the makers of ArchiCAD Allplan and Vectorworks) (Eastman, et al., 2011).

To understand the methodology used in this thesis work, an understanding of Revit families, Revit hierarchy, and Revit Parameters is required. Therefore, a brief description is made in this section.

2.3.1 Revit Families

A family is a group of elements with a standard set of properties, called parameters, and a related graphical representation (Autodesk , 2020). Any element (i.e., Instance in Revit

terminology) within the Revit is always under some family tag. Therefore, on a broader approach family are being classified into three main kinds:

• System Families: These are the predefined families in Revit, which cannot be created or deleted or saved externally or loaded from one project to another. Attributes of

families such as parametric, graphical, and documentation already exist for system families and cannot be defined.

Example: Wall, Roofs, Floors, Ducts, etc.

• Loadable Families: These are user-defined families, which can be created or modified

in Revit. As loadable families are created and save in external .rfa files format, they can be easily imported into different projects. For loadable families, attributes such as parametric, graphical, and documentation do not exist and are hence defined manually.

Example: Ready-made building and system components from the market such

as Windows, Doors, Furniture, Water heater, Plumbing fixtures, etc.

• In-Place Families: These are any in-place elements created within a specific project by using Revit geometric tools; Revit creates a specific in-place family for such an

element. In-Place Familie is a completely user-defined family. Example: Unique costume made table for a specific project.

7

2.3.2 Revit Hierarchy

Figure 4: Revit Hierarchy

• ‘Category’: It is the primary tag that covers all family sets within the Revit. A list of categories is predefined and cannot be changed.

• ‘Family’’: It is a set of particular elements belonging to a particular category. All elements belong to some family, which further belong to some category.

• ‘Type’: It is a single element within the family, defined by specific parametric and graphical rule. Type is created through the use of the family creator option. A set of

types together makes one single family. Usually, different types carry the same parameters but different dimensions.

• ‘Instance’: It is an actual object which is created in the project. Each instance is unique

and does not relate to the other. Any change made in the instance will not affect the other instance.

2.3.3 Revit Parameters Table 1: Classification of parameters (Molinos, 2016)

Based on the thesis requirement and Revit framework, new family parameters were created in family types.

System parameter Family parameter Project parameter Shared parameter

Predefined Custom defined Custom defined Custom defined

Basics parameters

which are always available for any

object

Only available in the particular family for

which it is created

Available for every object within the

project file

Externally shared between different

projects

Saved in a project file and family file

Saved in a family file

Saved in a project file

Saved in an external file apart from the

project

Appear in Schedules & tags

Not appear in Schedules & tags

Appear in Schedules, but not in tags

Appear in Schedules & tags

Category

Family

Type

Instance

8

2.4 Dynamo background Working of any software is just a couple of code lines that are continually executing the commands that we give it. Those commands are realized through a graphics processor that shows the imagery that user work with every day. The beneficial thing that Dynamo does is by

gives the user a way to transition the code that's operating behind the curtain into a visual display that users can wire together in smart ways and formats to create algorithms. These algorithms are further used to develop geometry. Dynamo then takes algorithmic relationships, provided modification of the code by a user, and feeds back into the program itself , which

generates the project's course as per user requirements. Therefore, Dynamo can be stated as a visual programming toolkit that aims to be accessible to both non-programmers and programmers alike. It gives designers and developers the potential to visually script behavior, defines custom pieces of logic, and script using various textual programming languages

(Autodesk, 2019). The main working principle of Dynamo is through the Application Programming Interface (API). API can be defined as a digital medium of communication between different software.

API works by sending a request for particular data from end A to end B and receiving it back through the same channel. It also allows users to easily manipulate data from end A to end B, and vice-vasa. As Revit provides a rich .NET API framework, it easily enables Dynamo to access Revit APIs (Ignatova, et al., 2018). Dynamo act as bi-directional data sharing medium,

unlike IFC uni-directional way. Through API, another concept of BIM interoperability is achieved through which data can quickly transfer between different packages. BIM interoperability has been discussed at the end of this report.

2.4.1 Dynamo workflow ‘Node’ is a processing block of Dynamo that can carry out operations like calculation, enabling input and output of value and extracting value from different software or creating geometry, etc. Depending on the node function, a node can have an input and output port or either of

them. Usually, the input port is on the left side, and the output port is on the right side of every node. Furthermore, the node's representation is by its name, followed by its category’s sub-categories name. An example of a Cuboid node can be seen in Figure 4 below.

Figure 5: Dynamo workflow

Nodes

Wire

Library

9

‘Wire’ is the medium of establishing a relationship between nodes. Through the wire, data is carried from one node to another. As the connection is formed by connecting the output of one node from its right side to the input of another node's left side, it can be said as the flow of data

in Dynamo is from left to right. ‘Library’ is the set of all nodes which can be used in visual programming. It contains predefined nodes, custom nodes, and external nodes added through the package manager. All nodes are

automatically arranged in hierarchical order, such as Library> Categories> Sub-categories> Node. For example, Figure 5 presents how node ‘Cuboid.ByLengths’ is accessed; Library>Geometry>Cuboid>ByLenght.

2.5 EPBD calculation process of SRI assessment EPBD method for SRI calculation uses a matrix kind of system of nine domains and seven

impact categories. Each domain has its own set of services, which needs to be rated between functionality 0 to 4, depending on their smartness. Then an aggregate score is made for each domain based on its services, then a vertical aggregated score is made based on all domains for individual impact category, and at last, a horizontal aggregated score of impact factors is

considered as the final SRI score (Verbeke, et al., 2020). Now to understand thoroughly, it is crucial to understand the terminology used in the SRI system. Therefore, an attempt is made to explain the SRI terminology in a simplified format.

Let us start with some definition: ‘Domains’: A set of service groups derived from the analysis and desk research of existing

studies regarding smart ready technologies in accordance with 'EN 15232:2017- Energy Performance of Buildings. Impact of Building Automation, Controls and Building Management'.

Figure 6: SRI structure (Verbeke , et al., 2017)

10

A total of 9 domains are formed:

• Heating

• Cooling

• Domestic hot water

• Controlled ventilation

• Lighting

• Dynamic building envelope

• Electricity

• Electric vehicle charging

• Monitoring and Control ‘Smart ready technology’: Any technology within the building, which provides communication

protocols or building automation system for its interaction with building occupants, is termed Smart ready technology. The term 'ready' stands for showing that technology is available in smart form but it is not being used yet. Such technology can either be in the form of ICT technology or physical hardware or a combination of both.

‘Smart ready service’: Any service within the building which uses smart ready technology for its operation is teamed as Smart ready service. For Simplified method A, Annex - I represents 27 smart services that have been considered.

‘Functionality level’: It can be defined as a scale of ordinal numbers ranging from 0 to 4, reflectings the technological advancement of the respective service. Here 0 means basic, and 4 means most advance. SRI committee had defined a set of functionality levels for each service,

refer Annex - I. ‘Impact category’: It can be defined as the main attribute of a building and its user, which is affected by the service smartness's level.

A total of 7 impact categories are formed-

• Energy efficiency

• Maintenance and fault prediction

• Comfort

• Convenience

• Health and wellbeing

• Information to occupants

• Grid flexibility and storage ‘Weighting factor’: Weightage of each domain within the individual impact category, which is

indicated by a percentage. With respect to the above definitions, the SRI team has proposed the following step by step procedure for the assessment:

Step 1: - Selections of relevant services through the triage process. Here, the idea is to select only services relevant to the building as per the location and omit the rest of them.

Step 2: - Define the functionality level of each smart-ready service.

11

Determine and assign the functionality level to every considered service, either by site visit or by reviewing technical building system documents.

Step 3: - Calculate the impact criterion scores.

where:

𝑑 = number of the technical domain in question, 𝑑 ∈ ℕ

𝑖𝑐 = number of the impact criterion in question, 𝑖𝑐 ∈ ℕ 𝑁𝑑 = total number of services in technical domain 𝑑, 𝑁𝑑 ∈ ℕ

𝑆𝑖,𝑑 = service 𝑖 of technical domain 𝑑, 𝑖 ∈ ℕ, 1 ≤ 𝑖 ≤ 𝑁S𝑑, 𝐹𝐿(𝑆𝑖,𝑑) = functionality level of service 𝑆𝑖,𝑑 as available in the building or building unit

𝐼𝑖𝑐(𝐹𝐿(𝑆𝑖,𝑑)) = impact criterion score of service 𝑆𝑖,𝑑 for impact criterion number 𝑖𝑐,

according to the service’s functionality level, 𝐼𝑖𝑐(𝐹𝐿(𝑆𝑖,𝑑)) ∈ ℕ 𝐼(𝑑, 𝑖𝑐) = impact criterion score of domain number 𝑑 for impact criterion number 𝑖𝑐,

𝐼(𝑑, 𝑖𝑐) ∈ ℕ.

Step 4: - Calculate maximum impact scores

where:

𝐹𝐿𝑚𝑎𝑥(𝑆𝑖,𝑑) = highest functionality level that service 𝑆𝑖,𝑑 could have according to the smart-ready service catalogue

𝐼𝑖𝑐(𝐹𝐿𝑚𝑎𝑥(𝑆𝑖,𝑑)) = impact criterion score of service 𝑆𝑖,𝑑 for its highest functionality level, which means the maximum impact criterion score of service 𝑆𝑖,𝑑 for

impact criterion number 𝑖𝑐

𝐼𝑚𝑎𝑥(𝑑, 𝑖𝑐) = maximum impact criterion score of domain number 𝑑 for impact criterion number 𝑖𝑐.

Step 5: - Smart-readiness scores for impact criteria

where:

𝑑 = number of the technical domain in question 𝑁 = total number of technical domains

𝑊𝑑,𝑖𝑐 = weighting factor expressed as a percentage of technical domain number 𝑑 for impact criterion number 𝑖𝑐

𝑆𝑅𝑖𝑐 = smart readiness score expressed as a percentage for impact criterion number 𝑖𝑐. Step 6: - Smart-readiness scores along with the three EPBD key capabilities

where:

𝑆𝑅𝑐 = smart readiness score for key capability 𝑐

𝑀 = total number of impact criteria, 𝑀 ∈ ℕ 𝑊𝑓(𝑖𝑐) = weighting factor expressed in percentage of impact criterion number 𝑖𝑐 for key

functionality 𝑓 𝑆𝑅𝑖𝑐 = smart readiness score of impact criterion number 𝑖𝑐.

12

Step 7: - Total smart-readiness score for a building or building unit

where:

𝑆𝑅 = total smart readiness score 𝑆𝑅𝑐 = smart readiness score of key capability 𝑐.

Step 8: - (Optional) Smart-readiness scores for technical domains.

2.5.1 Use of MS-Excel for user interface Based on the 2.5 calculation steps, an excel file was created by the SRI committee for providing

it as an interactive user tool. In this excel file, there are total 16 sheets, of which sheets number 2 to 5 are the main sheets for SRI calculation. Following is the step by step procedure for the use of an excel file:

I. The whole process starts with sheet 2, i.e., Building information sheet, where

assessors give an account of general building information such as building type and its usages, location with its net floor area, and construction year. Also, the

status of new or renovated is stated. Most important, selecting a weighting factor between predefined or user-defined will be used for the main SRI calculation. Then a selection of technical building systems is made by specifying their components.

II. Next, in sheet 3, i.e., the Calculation sheet, as per the predefined functionalities for each service, the main functionality level is assigned to all required services in sheet 3.

III. The final SRI score can then directly be obtained in sheet 4, i.e., Results sheet.

This sheet provides the final SRI score with an individual score for each domain and impact factor.

IV. Sheet 5, i.e., Weightings, is the sheet that provides the weighting factor for SRI calculation. In the sheet, the assessor can also define their own weighting

factors, if needed.

Appendix - A shows the user interface of different SRI excel sheets.

13

3. Methodology This chapter deals with the explanation of the method used in this thesis work. The first step of this thesis project was to understand the different approaches available for automation.

While considering different approaches, a particular focus was kept on developing a generalized Dynamo script for SRI score calculation, which can be used for residential and commercial building types. Because of such focus, a large number of building types can easily be included in future studies.

Therefore, two approaches were considered, that are:

i. Developing a full Dynamo script to perform complete SRI assessment within the Revit model.

ii. Developing a partial Dynamo script to extract only the services functionality score from the Revit model and send it to the original SRI excel file for its complete assessment.

For this thesis work, approach (ii) is considered.

3.1 Selection of the Revit model The selection of a proper BIM model is the most crucial part; therefore, a BIM model that is very close to a Digital twin of an existing building was used in this project. The BIM model of

BIMobject® Corporate HQ located at Studio Nordenskiöldsgatan 24, 211 19 Malmö, Sweden (BIMobject®, n.d.) was considered in this project. As the BIMobject® Corporate HQ is located on the 9th floor, staircase and elevator shafts can be noticed in Figure 7.

Figure 7: Original Revit model

To make a realistic approach, the following model was redesigned by removing all staircases

and elevators and making a separate main entrance door on the left-bottom wall; so that BIMobject® Corporate HQ can be considered as a single ground floor office building. By doing so, all required TBS for a single office building can individually be considered. Figure 8 represents the actual BIM model used in this project.

14

Figure 8: Actual Revit model used in this project

3.2 Initial data for SRI excel file

For filling up the Building information sheet of the SRI excel file, the following data is used in combination with few assumptions: a. General building information based on the BIM model,

• Building type: Non-residential

• Building usage: Office

• Location: Sweden

• Climate zone: North Europe

• Net floor area: 500 - 1000 m2

• Year of construction: After 2010

• Building state: Original

• Preferred weightings factor: Default

b. Through the triage process following technical building system are assumed with their

respective services,

• Heating system with emission types such as radiator connected to district heating and no thermal energy storage within the building.

• Cooling system with emission types such as through air and no cooling energy storage

within the building.

• Controlled ventilation through mechanical ventilation, which has a heat recovery unit. This ventilation can also be used in both space heating and cooling through air control.

• Occupancy based lighting system which can also be controlled manually.

• Dynamic envelope with automated windows & blinds.

• Central control panel for monitoring & control, which provides information regarding

TBS and BACS.

c. Within the total 27 services catalogue of Simplified method A and based on the above triage process, the following 14 services are considered for final SRI score calculation.

For Heating: • Heat emission control

15

• Heat generator control (all except heat pump)

• Report information regarding heating system performance

For Cooling: • Cooling emission control

• Generator control for cooling

• Report information regarding cooling system performance

For Controlled ventilation:

• Air flow control al the room level

• Reporting information regarding IAQ

For Lighting: • Occupancy control for indoor lighting For Dynamic envelope:

• Window solar shading control

• Reporting information regarding performance

For Monitoring & control:

• Interaction between TBS and/or BACS

• Smart Grid Integration

• Central reporting of TBS performance and energy use

Refer to Appendix - B for detailed information on selected smart services with their respective functionality level.

3.3 Allocation of Simplified method A services into the Revit model

Based on 3.2 and Appendix - B, selected smart services are assigned as ‘Family Parameters’ to the Revit Family Types. These Types are selected on empirical bases, i.e., by considering the working principal of their actual hardware in real life.

Following is the step by step explanation of the general procedure adopted in this task: Step-I: Selecting the appropriate Family Type Step-II: By using the Family editor option, getting inside the selected Family Type Step-III: Clicking on Family Type properties

Step-IV: Selecting ‘New parameter option’ from Family Type properties panel Step-V: In the New parameter panel, assigning the Parameter name as the required Smart service name, Discipline as Common, Type of parameter as Integer, and Group parameter under as Text. Also, selecting the Parameter type as a Family parameter.

Step-VI: Based on Simplified method A catalogue, assign the functionality level number in the parameter value section.

Refer to Appendix - C for a graphical representation of all the above steps. Appendix - C also

shows the allocation of all selected smart services within the appropriate Types.

16

3.4 Dynamo script for Revit to SRI excel sheet For developing a script, Dynamo Revit 2.0.4 is used, which is an embedded tool in Revit mainframe. This Dynamo version comes with a large number of predefined Revit nodes. Primary nodes which are used in the script are as follows:

‘Family Types’ This node facilitates selecting all specified family Type elements from the Revit model and provides them as an output for other nodes. Figure 9 represents the selection of different Type

through the Family Types node.

Figure 9: Example of a Family Type node

‘Code Block’

This node facilitates the ability to code directly in graphical format. As it is a universal node, it can fully be customized as per user requirement. Figure 10 represents the customized node used in this project.

Figure 10: Example of Code Block used for retrieving heating related service functionality

level

17

‘Get Parameter Value By Name’ This node facilitates obtaining the parameter value from the required Element Type. The input of Element and Parameter name provided through left side and Parameter value is obtained as

output through the right side. Figure 11 represents the GetParameterValueByName node.

Figure 11: Example of GetParameterValueByName node

Using the above nodes, an individual group was created for nine domains listed in the SRI calculation. Each of these groups consists of all the smart services listed in SRI Simplified

method A. Then, with the help of List.Join node, the functionality level values of all smart services were brought together, and then by using ExportExcel node, all functionality level values were sent to SRI excel file for final SRI score calculation. Figure 12 represents the complete Dynamo script of the project.

Figure 12: Complete Dynamo script

18

4. Results & Discussion The format of obtaining results through the presented semi-automated process is the same as

the original manual process because the same excel file is used in both cases. The excel file receives the smart functionality values from Dynamo script and processes them as per predefined formulae. The final score of SRI with individual domain and impact criteria can be found on the Results sheet of the excel file.

4.1 Validating the obtained result The credibility of any automation process is based on its final results. If the proposed semi-automation process can produce the same result as the manual process, it can be considered valid. Therefore, the comparison between results obtained from manual SRI calculation and

semi-automated SRI calculation had been made.

Figure 13: Results obtained through a manual process

Figure 14: Result obtain through a proposed semi-automated process

19

Figure 13 represents the results obtain by manually inputting the functionality values in the calculation sheet of the excel file, whereas Figure 14 represents the results obtained with the help of Revit and Dynamo. As the results in both the cases are precisely the same; therefore,

the proposed semi-automatic SRI calculation method is considered verified and successful.

4.2 BIM interoperability Another point noted during the project was that, even though the Dynamo script was developed

by considering all smart services from Simplified service A catalogue, it only extracted the functionality values of those 14 assigned services from the Revit model without giving any error. This proves the interoperability on the BIM platform, as those service functionality values from the Revit model are further directly supplied to the excel sheet in the required

sequence.

4.3 Effect of LOD When changes were made in Revit models by assigning them smart services as a parameter,

i.e., Revit objects were taken from LOD 300 to LOD 350, an increase in the Revit model's file size was noted. Therefore, it can be said that if more detailed family geometry is created, the larger the family file will get resulting in a larger Revit model file. Revit user should consider this point while working on a complex model with higher LOD, as it will start to degrade the

model performance sooner, and some tasks like selecting and moving many components can become a nightmare depending on system hardware.

20

5. Conclusion This thesis aimed to use the BIM platform for SRI assessment with objectives such as to

develop a semi-automatic SRI calculation process, explain digital changes happening with the BIM model during its modification and explain the BIM interoperability by extracting required data from BIM model and sending it other non-BIM application. After analyzing the obtained results, it is clear that SRI assessment can be automized through BIM. Due to the time frame,

only a semi-automated process was done in this project but there is a strong possibility of fully-automizing SRI assessment through BIM in the future. Moreover, this project can be considered as a benchmark for starting atomization of other

energy certification schemes. As the calculation methodology of any certification scheme is always constant, with Dynamo bidirectional nature, we can easily develop a required Dynamo script to perform similar calculation through Revit BIM model (subject to the condition, the BIM model is also developed to required extend). Integration of such vision will give

robustness for adopting the BIM concept to a larger extend and will provide a chance to use the BIM model to its full potential. Developing a one-time automated energy calculation process of any energy certification scheme will save energy consultants' considerable time and bring more accurate results.

Other benefits of automation through BIM can be stated as, if there is any potential problem with building such as high energy consumption, it will be detected and rectified beforehand. This will also save the client’s time and money in the future, which will provide a high return

on client investment on BIM.

21

6. Future scope During the thesis work, it was noted that there is no established relationship between different

artifacts within any system. Therefore, when an attempt was made for automation, it was completely done on empirical bases. The final result of proposed semi-automated SRI methodology yields same result as of manual calculation but if someone wants to develop an automize calculation process for some different certification system, then it will again be going

to be on empirical bases. The responsible researcher will again have to make own assumption for connecting different artifacts for the respective theme and will have to spend lot of time. Overall, automation will be possible but with the expense of have time.

Therefore, author of this thesis strongly proposes the development of an Ontology for future automation work. Ontology means representation of types of entities in a given domain & of the relations between them. Setting up ontology can be the key for atomization of construction industry. This is because, only with the ontology we can bring different system/database to

common working grounds. In addition to the development of Ontology, an approach of generative design in future SRI automation is also recommended by author. If generative design is included in future work, it

will provide a possibility to obtain best SRI score by automatically simulating N-numbers of smart services combination for a building, based on its given attributes. This will provide huge margin in energy saving.

22

References

America, A. G. C, 2006. The contractors guide to BIM. [Online]

Available at: https://www.engr.psu.edu/ae/thesis/portfolios/2008/tjs288/Research/AGC_GuideToBIM.pdf [Accessed 23 October 2020].

Autodesk , 2020. About families. [Online] Available at: https://knowledge.autodesk.com/support/revit-products/learn-explore/caas/CloudHelp/cloudhelp/2021/ENU/Revit-Model/files/GUID-6DDC1D52-E847-

4835-8F9A-466531E5FD29-htm.html [Accessed 26 October 2020].

Autodesk, 2019. What is Dynamo. [Online] Available at: https://primer.dynamobim.org/01_Introduction/1-2_what_is_dynamo.html [Accessed 29 October 2020].

Bain, D., 2020. 10 things we’ve learnt from the 2020 BIM Survey. [Online] Available at: https://www.thenbs.com/knowledge/10-things-weve-learnt-from-the-2020-bim-survey

[Accessed 20 October 20].

BIMobject®, n.d. 9th Floor Studio BIMobject HQ. [Online]

Available at: https://www.bimobject.com/en/bimobjectmodels/product/bimobject_hq [Accessed 29 June 2020].

Conant, D., 2013. Revit Timeline. [Online] Available at: https://web.archive.org/web/20131226044017/http://forums.augi.com/showthread.php?20803-Revit-Timeline-(W-I-P-)


Designing building Wiki, 2020. BIM software. [Online]

Available at: https://www.designingbuildings.co.uk/wiki/Building_information_modelling_BIM#Software [Accessed 23 October 2020].

Eastman, C. M. et al., 2011. BIM handbook: A guide to building information modeling for owners, managers, designers, engineers and contractors. s.l.:John Wiley & Sons.

Ellis, B. A., 2006. Building information modeling: an informational tool for stakeholders. In Government/Industry Forum by the Federal Facilities Council, pp. 1-5.

European Commission, 2019. Energy performance of buildings directive. [Online] Available at: https://ec.europa.eu/energy/topics/energy-efficiency/energy-efficient-buildings/energy-performance-buildings-directive_en#energy-performance-of-buildings-

standards [Accessed 28 September 2020].

European Parliament, Council of the European Union, 2018. Document 32018L0844. [Online] Available at: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32018L0844 [Accessed 28 September 2020].

Fischer, M. & Agrawal, A., 2020. Digital Twin for Construction. [Online] Available at: https://cife.stanford.edu/Seed2019%20DigitalTwin


23

Gu, N. & London, K., 2010. Understanding and facilitating BIM adoption in the AEC industry. Automation in Construction, December, 19(8), pp. 988-999.

Haymaker, J., Kam, C. & Fischer, M., 2005. A methodology to plan, communicate and control multidisciplinary design processes. Construction Informatics Digital Library, pp. 408-419.

Ignatova, E., Zotkin, S. & Zotkina, I., 2018. The extraction and processing of BIM data. s.l., IOP Publishing, pp. Vol. 365, No. 6.

Latiffi, A. A., Brahim, J. & Fathi, M. S., 2014. The Development of Building Information Modeling (BIM) Definition. Applied Mechanics and Materials (Vol. 567), 06 06.pp. 625-630.

Mcauley, B. & Behan, A., n.d. BIM: Enabling Energy Management and Control for the Construction Sector. [Online]

Available at: https://energybimcert.eu/?page_id=2774 [Accessed 21 October 2020].

Molinos, R., 2016. Model Parameters. [Online] Available at: https://www.modelical.com/en/gdocs/parameters/ [Accessed 27 October 2020].

National Institute of Building Sciences, 2019. FAQ. [Online] Available at: https://www.nationalbimstandard.org/faqs#faq1 [Accessed 21 October 2020].

NBS, 2017. National BIM Report 2017, s.l.: s.n.

Omar, M. F., Mohd Nawi, M. N. & Nursal, A. T., 2014. Towards the significance of decision aid in Building Information Modeling (BIM) software selection process. In E3S Web of Conferences, Volume 3, pp. 1-6.

Pakhale, P. & Pal, A., 2020. Digital project management in infrastructure project: a case study of Nagpur Metro Rail Project. Asian Journal of Civil Engineering, pp. 1-9.

Popov, V., Mikalauskas, S., Migilinskas, D. & Vainiūnas, P., 2006. Complex usage of 4D information modelling concept for building design, estimation, sheduling and determination

of effective variant. Technological and economic development of economy, 12(2), pp. 91-98.

Reinhardt, J. & Bedrick, J., 2019. Level of Development specification part-1 & commentry

for Building information models & Data, s.l.: s.n.

United Nations, 2018. Department of Economic and Social Affairs. [Online]

Available at: https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html [Accessed 08 October 2020].

Verbeke , S. et al., 2017. Support for setting up a Smart Readiness Indicator for buildings and related impact assessment, Interim report, s.l.: s.n.

Verbeke, S. et al., 2020. FINAL REPORT ON THE TECHNICAL SUPPORT TO THE DEVELOPMENT OF A SMART READINESS INDICATOR FOR BUILDINGS, Luxembourg: Publications Office of the EU, 2020.

Vito, 2017. About us. [Online] Available at: https://smartreadinessindicator.eu/about-us

[Accessed 28 September 2020].

24

Annex - I This complete summarized smart service catalogue is taken form (Verbeke, et al., 2020).

Domain Smart ready

service

Functionality level 0 (as non-smart

default)

Functionality level 1




Heating Heat emission

control

No automatic

control

Central automatic

control (e.g. central

thermostat)

Individual room control (e.g. thermostatic

valves, or electronic controller)

Individual

room control with

communication between

controllers and

to BACS

Individual room control

with

communication and presence

control

Heating

Heat generator

control (all except heat

pumps)

Constant

temperature control

Variable temperature

control depending on

outdoor

temperature

Variable

temperature control

depending on

the load (e.g. depending on

supply water temperature set

point)

Heating Heat generator control (heat

pumps)

On/Off-control of heat

generator

Multi-stage

control of heat generator

capacity depending on

the load or

demand (e.g. on/off of several

compressors)

Variable control

of heat generator capacity

depending on the load or

demand (e.g. hot gas bypass,

inverter

frequency control)

Variable control of heat

generator


the load AND external

signals from

grid

Heating Storage and shifting of

thermal energy None

HW storage vessels

available

HW storage vessels

controlled based on external

signals (from

BACS or grid)

Heating

Report information regarding

heating system performance

None

Central or remote

reporting of current

performance

KPIs (e.g. temperatures, submetering

energy usage)

Central or

remote reporting of current

performance

KPIs and historical data

Central or remote

reporting of performance evaluation

including forecasting

and/or

benchmarking

Central or remote

reporting of performance evaluation


and/or

benchmarking; also including

predictive management

and fault

detection

25

Domestic

hot water

Control of

DHW storage charging (with direct electric

heating or integrated

electric heat

pump)

Automatic control on /

off

Automatic control on /

off and

scheduled charging enable

Automatic on/off control,

scheduled

charging enables and

demand-based

supply temperature

control or multi-sensor storage management

Domestic hot water

Control of DHW storage

charging

None HW storage

vessels

available

Automatic charging control

based on local availability of renewables or

information from electricity grid (DR, DSM)

Domestic hot water

Report information

regarding domestic hot

water

performance

None

Indication of actual values

(e.g. temperatures, submetering

energy usage)

Actual values

and historical data

Performance evaluation


and/or

benchmarking



and/or


predictive management

and fault

detection

Cooling

Cooling

emission control

No automatic

control

Central automatic

control (e.g. central

thermostat)

Individual room control (e.g. thermostatic

valves, or electronic controller)

Individual

room control with

communication between

controllers and

to BACS

Individual

room control with

communication and

occupancy

detection

Cooling Generator control for

cooling

On/Off-control of

cooling

production

Multi-stage

control of cooling

production


the load or demand (e.g.

on/off of

several compressors)

Variable control

of cooling production capacity

depending on the load or

demand (e.g. hot gas bypass,

inverter

frequency control)

Variable

control of cooling

production capacity

depending on

the load AND external

signals from

grid

Cooling Flexibility and

grid

interaction

No automatic

control

Scheduled operation of

cooling system

Self-learning optimal control

of cooling system

Cooling

system capable of

flexible

control through grid signals (e.g.

DSM)

Optimized control of

cooling system based

on local

predictions and grid

signals (e.g. through model

26

predictive control)

Cooling

Report information regarding

cooling system

performance

None

Central or

remote reporting of

current

performance KPIs (e.g.

temperatures,

submetering energy usage)

Central or remote reporting

of current

performance KPIs and

historical data

Central or

remote reporting of performance

evaluation including

forecasting

and/or benchmarking

Central or

remote reporting of performance

evaluation including

forecasting

and/or benchmarking;

also including predictive

management

and fault detection

Controlled

ventilation

Supply air

flow control at the room level

No ventilation system or

manual control

Clock control

Occupancy

detection control

Central Demand

Control based

on air quality sensors (CO2,

VOC,...)

Local Demand Control based

on air quality sensors (CO2,

VOC,...) with local flow from/to the

zone regulated by dampers

Controlled ventilation

Reporting information

regarding IAQ None

Air quality sensors (e.g.

CO2) and real time

autonomous

monitoring

Real time monitoring &

historical information of

IAQ available to

occupants

Real time monitoring &

historical

information of IAQ available

to occupants + warning on

maintenance

needs or occupant

actions (e.g.

window opening)

Lighting

Occupancy

control for indoor lighting

Manual on/off

switch

Manual on/off switch +

additional

sweeping extinction

signal

Automatic detection (auto

on / dimmed or auto off)

Automatic detection

(manual on / dimmed or auto off)

Dynamic building

envelope

Window solar shading

control

No sun shading or

only manual

operation

Motorized operation with

manual

control

Motorized

operation with automatic

control based on sensor data

Combined light/blind/HV

AC control

Predictive

blind control (e.g. based on

weather forecast)

27

Dynamic building

envelope


regarding performance

No reporting

Position of each product

& fault detection

Position of each product, fault detection &

predictive maintenance

Position of each product,

fault detection, predictive

maintenance,

real-time sensor data (wind, lux,

temperature…)

Position of each product, fault detection,

predictive maintenance, real-time &

historical sensor data

(wind, lux, temperature…

)

Electricity

Storage of (locally

generated) electricity

None

On site storage of electricity

(e.g. electric battery)

On site storage

of energy (e.g. electric battery

or thermal storage) with

controller based

on grid signals

On site storage of energy (e.g.

electric battery or thermal

storage) with

controller optimising the

use of locally generated electricity

On site storage

of energy (e.g. electric battery

or thermal

storage) with controller

optimising the use of locally

generated

electricity and possibility to

feed back into

the grid

Electricity

Reporting information regarding

electricity consumption

None

reporting on

current electricity

consumption on building

level

real-time feedback or

benchmarking

on building level


benchmarking

on appliance level


benchmarking on appliance

level with

automated personalized

recommendati

ons

Electricity


regarding local electrcity

generation

None

Current

generation data available

Actual values

and historical data



and/or

benchmarking



and/or


predictive

management and fault

detection

Electricity


regarding energy storage

None

Current state of charge

(SOC) data available

Actual values and historical

data

Performance evaluation including

forecasting and/or

benchmarking

Performance evaluation including

forecasting and/or

benchmarking;

also including predictive

management and fault detection

Electric vehicle

charging

Charging capacity

not present ducting (or

simple power plug) available

0-9% of parking spaces has recharging

points

10-50% or parking spaces has recharging

point

>50% of parking spaces has recharging

point

28

Electric vehicle

charging

EV Charging Grid balancing

Not present (uncontrolled

charging)

1-way controlled

charging (e.g.

including desired

departure time

and grid signals for

optimization)

2-way controlled

charging (e.g.

including desired

departure time

and grid signals for

optimization)

Electric

vehicle charging

EV charging

information and

connectivity

No

information available

Reporting information on

EV charging status to

occupant

Reporting information on EV charging

status to occupant AND

automatic

identification and

authorization of the driver to the charging station

(ISO 15118 compliant)

Monitoring and control

Single platform that

allows

automated control &

coordination between TBS + optimization

of energy flow based on

occupancy ,

weather and grid signals

None

Single

platform that allows manual

control of

multiple TBS

Single platform that allows

automated control &

coordination

between TBS

Single platform that

allows

automated control &

coordination between TBS + optimization

of energy flow based on

occupancy,

weather and grid signals

Monitoring and control

Smart Grid Integration

None - No harmonization between grid

and TBS; building is

operated independently from the grid

load

Demand side management possible for

(some) individual

TBS, but not coordinated over various

domains

Coordinated demand side

management of

multiple TBS

Monitoring

and control

Central reporting of

TBS performance

and energy use

None

Central o rremote

reporting of

realtime energy use per energy carrier

Central or remote reporting

of realtime energy use per

energy carrier, combining TBS

of at least 2

domains in one interface

Central or remote

reporting of realtime

energy use per

energy carrier, combining TBS of all

domains in one interface

29

Appendix - A User interface of different SRI excel sheets

Sheet 2 to 5

30

Appendix - B Detail information on selected smart services with their respective functionality level.

31

Gray colour in above tables indicated the omitted smart services.

32

Appendix - C Step by step procedure for allocating smart services into Revit model For Heating services.

Step-I: Selecting the Type 300-1200mm for allocating heating services (same services are also allocated for Type 300-800mm & 300-1000mm).

Step-II: By using Family editor option, getting inside the selected Family Type

33

Step-III: Clicking on Family Type properties

Step-IV: Selecting ‘New parameter option’ from Family Type properties panel

34

Step-V: In New parameter panel, assigning Parameter name as required Smart service name, Discipline as Common, Type of parameter as Integer and Group parameter under as Text. Also, selecting Parameter type as Family parameter.

Step-VI: Based on Simplified method A catalogue, assign the functionality level number in

parameter value section.

35

For Cooling services

For Control ventilation services

36

For Lighting service

For Dynamic envelope

37

For Monitoring & control

TRITA-ABE-MBT-2134

www.kth.se

Documents

Utilization of BIM platform for automatized energy